Voir la source de Classification naïve bayésienne multinomiale

== Définition ==
[[Classification naïve bayésienne]] où les attributs suivent une [[distribution multinomiale]]. 

==Compléments== 
On emploie parfois ''polynomiale'' à la place de ''multinomiale'' et ''bayésienne naïve'' plutôt que ''naïve bayésienne''.

== Français ==
''' classification naïve bayésienne multinomiale '''

''' classification naïve de Bayes multinomiale '''

''' classification naïve bayésienne polynomiale '''

''' classification naïve de Bayse polynomiale '''

== Anglais ==
''' multinomial naïve Bayes classification'''

<!-- With a multinomial event model, samples (feature vectors) represent the frequencies with which certain events have been generated by a multinomial {\displaystyle (p_{1},\dots ,p_{n})}(p_1, \dots, p_n) where {\displaystyle p_{i}}p_{i} is the probability that event i occurs (or K such multinomials in the multiclass case). A feature vector {\displaystyle \mathbf {x} =(x_{1},\dots ,x_{n})}{\mathbf  {x}}=(x_{1},\dots ,x_{n}) is then a histogram, with {\displaystyle x_{i}}x_{i} counting the number of times event i was observed in a particular instance. This is the event model typically used for document classification, with events representing the occurrence of a word in a single document (see bag of words assumption). The likelihood of observing a histogram x is given by

--> 
==Sources==

[https://en.wikipedia.org/wiki/Naive_Bayes_classifier#Multinomial_na%C3%AFve_Bayes  Source : Wikipedia  Machine Learning ]

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